Spectral efficiency is increasingly important in today's mobile communications. The non-constant envelope (high peak-to-average ratio) digital modulation schemes used in many 2.5G and 3G wireless systems make RF power amplifier (PA) linearity and efficiency a crucial design issue. Typically, linearity is achieved either by reducing efficiency or by using linearization techniques. For a Class A PA, simply ‘backing off’ the input can improve linearity, but this reduces power efficiency and increases heat dissipation. When considering the vast numbers of base stations wireless operators need to account for, increased power consumption is not a realistic possibility, and linearization techniques are therefore required.
Digital pre-distortion (DPD) has become the most important linearization technique for nonlinear devices such as Base Station Power Amplifiers and frequency mixers. The basic concept of a pre-distorter is to calculate the inverse nonlinearity function of the nonlinear device. The pre-distortion is applied to the baseband signal before modulation, up-conversion, and amplification by the PA, so as to reduce Intermodulation (IM). The nonlinear expression can be implemented with polynomials or Look-up Tables (LUTs). The nonlinearity function is usually a simplified Volterra series that characterizes the inputs together with the “memory effects” of the device for better linearization performance. When a PA is stimulated with signals with low average power variation, the learning of the DPD can reach quite good precision. Unfortunately, in practice, signals are very seldom continuous in power. They vary for each timeslot depending on different power schemes. Every time the power is applied (i.e. there is a transient in power) the DPD and PA will together generate a residual IM which is the mismatch of the pre-distorter spectrum and PA spectrum. The amount of transient IM is dependent on the difference in PA characteristics between the average state and transient state.
Consider, for example, a PA in a Base Station operating Time-Division Duplex (TDD). In TDD operation the PA will be turned off when transmission is not taking place, to reduce the noise in the receiver and unnecessary power consumption. This procedure means that the PA will be in a transition state during the whole TX transmitting period. This is illustrated in FIG. 1, which is a representation of a typical power output for such a PA varying with time. The signal 1 resembles a square wave, in which power is applied 2a, is then maintained at a high value for a short period of time 3a (for TX transmission) and is then switched off 4a. The TX transmission period 3a is too short for a steady state to be reached, so any DPD will need to change throughout the period of the TX transmission burst 3a. This process will need to be repeated for each TX burst 3b, 3c. One way of addressing this problem is to have a number of characterizations of the system as a function of temperature, current, input power, etc.
Existing approaches are often in terms of static DPD. The DPD is adapted with samples from somewhere in the transmit burst 3a which will probably be wrong for subsequent TX bursts 3b, 3c. There are other solutions to similar problems like multidimensional sets of DPD parameters, or equivalent. The linearization schemes would inevitably get very complicated and expensive and the performance would be dependent on the accuracy of these time varying constants.
Alternatively, the DPD parameters could be remembered from the end of one burst 4a and applied at the beginning of the next burst 2b. This is a compensation for a high power characteristic of the PA, but the PA has undergone a shut down in between. In other words, the PA may have reached a steady state of temperature, current etc. by the time the PA is shut down 4a. When it is re-activated 2b these conditions will be different as a result of the intervening shutdown, and this correction will not be sufficient to reduce the entire IM. A transient IM will then be left until either the DPD is updated or the PA behaviour changes back to the adapted state.
This can be understood with reference to FIG. 2, which illustrates how the IM can be reduced through the course of each burst 3a, 3b, 3c. If the state of the DPD (i.e. the values in the LUT or parameters in the polynomials) is remembered from the end of the previous burst, the transient IM at the start of the next burst will be large again, because the conditions at the start of the burst are different to the conditions at the end of the previous burst.
It would be desirable to design a DPD scheme in which the transient IM at the start of each burst is reduced.